modify pipeline for "update_by_data";

fix bug in speciate. currently, node_delete and conn_delete can successfully work

tensorneat/problem/func_fit/func_fit.py

@@ -49,7 +49,10 @@ class FuncFit(BaseProblem):
             state, self.inputs, params
         )
         inputs, target, predict = jax.device_get([self.inputs, self.targets, predict])
-        loss = self.evaluate(state, randkey, act_func, params)
+        if self.return_data:
+            loss, _ = self.evaluate(state, randkey, act_func, params)
+        else:
+            loss = self.evaluate(state, randkey, act_func, params)
         loss = -loss
 
         msg = ""

tensorneat/problem/func_fit/xor.py

@@ -4,14 +4,19 @@ from .func_fit import FuncFit
 
 
 class XOR(FuncFit):
-
     @property
     def inputs(self):
-        return np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
+        return np.array(
+            [[0, 0], [0, 1], [1, 0], [1, 1]],
+            dtype=np.float32,
+        )
 
     @property
     def targets(self):
-        return np.array([[0], [1], [1], [0]])
+        return np.array(
+            [[0], [1], [1], [0]],
+            dtype=np.float32,
+        )
 
     @property
     def input_shape(self):

tensorneat/problem/func_fit/xor3d.py

@@ -16,12 +16,16 @@ class XOR3d(FuncFit):
                 [1, 0, 1],
                 [1, 1, 0],
                 [1, 1, 1],
-            ]
+            ],
+            dtype=np.float32,
         )
 
     @property
     def targets(self):
-        return np.array([[0], [1], [1], [0], [1], [0], [0], [1]])
+        return np.array(
+            [[0], [1], [1], [0], [1], [0], [0], [1]],
+            dtype=np.float32,
+        )
 
     @property
     def input_shape(self):